Removal of the LahtiWAdata and HintikkaXOData datasets #38 (#39)

* Removal of the LahtiWAdata and HintikkaXOData datasets #38

* Removal of the LahtiWAdata and HintikkaXOData datasets #38

* Delete .DS_Store

* Delete inst/.DS_Store

* Delete microbiomeDataSets.Rproj

* update gitignore

.gitignore

@@ -1,4 +1,6 @@
+.DS_Store
 inst/extras/
+.Rproj
 .Rproj.user
 .Rhistory
 .RData

DESCRIPTION

@@ -49,5 +49,5 @@ Suggests:
     SingleCellExperiment,    
     testthat
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 VignetteBuilder: knitr

NAMESPACE

@@ -27,4 +27,5 @@ importFrom(TreeSummarizedExperiment,TreeSummarizedExperiment)
 importFrom(TreeSummarizedExperiment,changeTree)
 importFrom(ape,read.tree)
 importFrom(methods,as)
+importFrom(utils,data)
 importFrom(utils,read.csv)

R/GrieneisenTSData.R

@@ -1,98 +1,98 @@
-#' Retrieve GrieneisenTS data
-#'
-#' Obtain longitudinal gut microbiome data in wild baboons from
-#' Grieneisen et al. (2021).
-#'
-#' @details
-#' The GrieneisenTS dataset contains 16,234 16S rRNA gene
-#' sequencing-based microbiome profiles from 585 baboon samples
-#' collected over 14 years to determine the heritability of the
-#' gut microbiome on various environmental factors such as
-#' diet, age, season. Each baboon had an average of 28 samples
-#' collected over 4.5 years. The data set can be used to
-#' investigate significance of longitudinal sampling at large
-#' sample sizes.
-#'
-#' This data set contains the 613 most prevalent taxa with a 
-#' phylogenetic tree.
-#'
-#' Column metadata includes the following fields:
-#'\itemize{
-#'  \item{sample: } {Sample ID (character)}
-#'  \item{baboon_id: } {Baboon ID (factor)}
-#'  \item{collection_date: } {Sample collection date (date ; YYYY-MM-DD)}
-#'  \item{sex: } {Sex (factor; F/M)}
-#'  \item{age: } {Age (numeric)}
-#'  \item{social_group: } {Social group ID (factor)}
-#'  \item{group_size: } {Social group size (integer)}
-#'  \item{rain_month_mm: } {Rain per month(mm) (numeric)}
-#'  \item{season: } {Season (factor; dry/wet)}
-#'  \item{hydro_year: } {Hydro year (integer)}
-#'  \item{month: } {Month (integer)}
-#'  \item{readcount: } {Read count (numeric)}
-#'  \item{plate: } {Plate (factor)}
-#'  \item{post_pcr_dna_ng: } {Post PCR DNA(ng) (numeric)}
-#'  \item{diet_PC1: } {Diet Principal coordinate 1 (numeric)}
-#'  \item{diet_PC2: } {Diet Principal coordinate 2 (numeric)}
-#'  \item{diet_PC3: } {Diet Principal coordinate 3 (numeric)}
-#'  \item{diet_PC4: } {Diet Principal coordinate 4 (numeric)}
-#'  \item{diet_PC5: } {Diet Principal coordinate 5 (numeric)}
-#'  \item{diet_PC6: } {Diet Principal coordinate 6 (numeric)}
-#'  \item{diet_PC7: } {Diet Principal coordinate 7 (numeric)}
-#'  \item{diet_PC8: } {Diet Principal coordinate 8 (numeric)}
-#'  \item{diet_PC9: } {Diet Principal coordinate 9 (numeric)}
-#'  \item{diet_PC10: } {Diet Principal coordinate 10 (numeric)}
-#'  \item{diet_PC11: } {Diet Principal coordinate 11 (numeric)}
-#'  \item{diet_PC12: } {Diet Principal coordinate 12 (numeric)}
-#'  \item{diet_PC13: } {Diet Principal coordinate 13 (numeric)}
-#'  \item{diet_shannon_h: } {Dietary Shannon's H index (numeric)}
-#'  \item{asv_richness: } {Amplicon sequence variant (ASV) richness (integer)}
-#'  \item{asv_shannon_h: } {ASV Shannon's H index (numeric)}
-#'  \item{pc1_bc: } {Principal coordinate 1 Bray-Curtis dissimilarity (numeric)}
-#'  \item{pc2_bc: } {Principal coordinate 2 Bray-Curtis dissimilarity (numeric)}
-#'  \item{pc3_bc: } {Principal coordinate 3 Bray-Curtis dissimilarity (numeric)}
-#'  \item{pc4_bc: } {Principal coordinate 4 Bray-Curtis dissimilarity (numeric)}
-#'  \item{pc5_bc: } {Principal coordinate 5 Bray-Curtis dissimilarity (numeric)}
-#'}
-#'
-#' Row metadata of the microbiome data contains taxonomic information on the
-#' Domain, Phylum, Class, Order, Family, Genus, and ASV levels.
-#'
-#' The row tree consists of a phylogenetic tree build using sequence
-#' information of 613 taxa.
-#'
-#' As reference sequences the ASV are provided.
-#'
-#' @return A \linkS4class{TreeSummarizedExperiment} object.
-#'
-#' @author Yagmur Simsek and Leo Lahti
-#'
-#' @references
-#' Grieneisen et al. (2021):
-#' Gut microbiome heritability is nearly universal but
-#' environmentally contingent
-#' \emph{Science}
-#' 373:6551 \url{https://science.sciencemag.org/content/373/6551/181.full}
-#'
-#' @name GrieneisenTSData
-#' @aliases baboongut
-#' @export
-#'
-#' @examples
-#' tse <- GrieneisenTSData()
-#'
-GrieneisenTSData <- function() {
-    dataset <- "3.14/grieneisen-ts"
-    tse <- .create_tse(dataset,
-                    assays = "counts",
-                    has.rowdata = TRUE,
-                    has.coldata = TRUE,
-                    has.rowtree = TRUE,
-                    has.refseq = TRUE,
-                    prefix = NULL)
-    tse
-}
-
-#' @rdname GrieneisenTSData
-#' @export
-baboongut <- GrieneisenTSData
+#' Retrieve GrieneisenTS data
+#'
+#' Obtain longitudinal gut microbiome data in wild baboons from
+#' Grieneisen et al. (2021).
+#'
+#' @details
+#' The GrieneisenTS dataset contains 16,234 16S rRNA gene
+#' sequencing-based microbiome profiles from 585 baboon samples
+#' collected over 14 years to determine the heritability of the
+#' gut microbiome on various environmental factors such as
+#' diet, age, season. Each baboon had an average of 28 samples
+#' collected over 4.5 years. The data set can be used to
+#' investigate significance of longitudinal sampling at large
+#' sample sizes.
+#'
+#' This data set contains the 613 most prevalent taxa with a 
+#' phylogenetic tree.
+#'
+#' Column metadata includes the following fields:
+#'\describe{
+#'  \item{sample}{Sample ID (character)}
+#'  \item{baboon_id}{Baboon ID (factor)}
+#'  \item{collection_date}{Sample collection date (date; YYYY-MM-DD)}
+#'  \item{sex}{Sex (factor; F/M)}
+#'  \item{age}{Age (numeric)}
+#'  \item{social_group}{Social group ID (factor)}
+#'  \item{group_size}{Social group size (integer)}
+#'  \item{rain_month_mm}{Rain per month(mm) (numeric)}
+#'  \item{season}{Season (factor; dry/wet)}
+#'  \item{hydro_year}{Hydro year (integer)}
+#'  \item{month}{Month (integer)}
+#'  \item{readcount}{Read count (numeric)}
+#'  \item{plate}{Plate (factor)}
+#'  \item{post_pcr_dna_ng}{Post PCR DNA(ng) (numeric)}
+#'  \item{diet_PC1}{Diet Principal coordinate 1 (numeric)}
+#'  \item{diet_PC2}{Diet Principal coordinate 2 (numeric)}
+#'  \item{diet_PC3}{Diet Principal coordinate 3 (numeric)}
+#'  \item{diet_PC4}{Diet Principal coordinate 4 (numeric)}
+#'  \item{diet_PC5}{Diet Principal coordinate 5 (numeric)}
+#'  \item{diet_PC6}{Diet Principal coordinate 6 (numeric)}
+#'  \item{diet_PC7}{Diet Principal coordinate 7 (numeric)}
+#'  \item{diet_PC8}{Diet Principal coordinate 8 (numeric)}
+#'  \item{diet_PC9}{Diet Principal coordinate 9 (numeric)}
+#'  \item{diet_PC10}{Diet Principal coordinate 10 (numeric)}
+#'  \item{diet_PC11}{Diet Principal coordinate 11 (numeric)}
+#'  \item{diet_PC12}{Diet Principal coordinate 12 (numeric)}
+#'  \item{diet_PC13}{Diet Principal coordinate 13 (numeric)}
+#'  \item{diet_shannon_h}{Dietary Shannon's H index (numeric)}
+#'  \item{asv_richness}{Amplicon sequence variant (ASV) richness (integer)}
+#'  \item{asv_shannon_h}{ASV Shannon's H index (numeric)}
+#'  \item{pc1_bc}{Principal coordinate 1 Bray-Curtis dissimilarity (numeric)}
+#'  \item{pc2_bc}{Principal coordinate 2 Bray-Curtis dissimilarity (numeric)}
+#'  \item{pc3_bc}{Principal coordinate 3 Bray-Curtis dissimilarity (numeric)}
+#'  \item{pc4_bc}{Principal coordinate 4 Bray-Curtis dissimilarity (numeric)}
+#'  \item{pc5_bc}{Principal coordinate 5 Bray-Curtis dissimilarity (numeric)}
+#'}
+#'
+#' Row metadata of the microbiome data contains taxonomic information on the
+#' Domain, Phylum, Class, Order, Family, Genus, and ASV levels.
+#'
+#' The row tree consists of a phylogenetic tree build using sequence
+#' information of 613 taxa.
+#'
+#' As reference sequences the ASV are provided.
+#'
+#' @return A \linkS4class{TreeSummarizedExperiment} object.
+#'
+#' @author Yagmur Simsek and Leo Lahti
+#'
+#' @references
+#' Grieneisen et al. (2021):
+#' Gut microbiome heritability is nearly universal but
+#' environmentally contingent
+#' \emph{Science}
+#' 373:6551 \url{https://science.sciencemag.org/content/373/6551/181.full}
+#'
+#' @name GrieneisenTSData
+#' @aliases baboongut
+#' @export
+#'
+#' @examples
+#' tse <- GrieneisenTSData()
+#'
+GrieneisenTSData <- function() {
+    dataset <- "3.14/grieneisen-ts"
+    tse <- .create_tse(dataset,
+                    assays = "counts",
+                    has.rowdata = TRUE,
+                    has.coldata = TRUE,
+                    has.rowtree = TRUE,
+                    has.refseq = TRUE,
+                    prefix = NULL)
+    tse
+}
+
+#' @rdname GrieneisenTSData
+#' @export
+baboongut <- GrieneisenTSData

R/microbiomeDataSets.R

@@ -7,9 +7,40 @@
 #' Bioconductor project. It is loaded as \code{TreeSummarizedExperiment} 
 #' object or a \code{MultiAssayExperiment} objects.
 #' 
+#' @section Deprecated functions:
+#' The following functions are deprecated and will be removed in a future 
+#' release:
+#' \itemize{
+#'   \item \code{LahtiWAdata}: Please use the \code{data()} function from the 
+#'   \code{mia} package to load this dataset.
+#'   \item \code{HintikkaXOData}: Please use the \code{data()} function from 
+#'   the \code{miaViz} package to load this dataset.
+#' }
+#'
 #' @name microbiomeDataSets-package
 NULL
 
 #' @importFrom BiocGenerics updateObject
 #' @importFrom methods as
-NULL
+NULL
+
+# Wrapper for LahtiWAdata
+LahtiWAdata <- function() {
+    .Deprecated("This function is deprecated and will be removed in a future 
+                release. Please load the dataset directly using the `data()` 
+                function from the mia/miaViz/mitTime packages.")
+    data_env <- new.env()
+    data("LahtiWAdata", package = "mia", envir = data_env)
+    return(data_env$LahtiWAdata)
+}
+
+# Wrapper for HintikkaXOData
+HintikkaXOData <- function() {
+    .Deprecated("This function is deprecated and will be removed in a future 
+                release. Please load the dataset directly using the `data()` 
+                function from the mia/miaViz/mitTime packages.")
+    data_env <- new.env()
+    data("HintikkaXOData", package = "miaViz", envir = data_env)
+    return(data_env$HintikkaXOData)
+}
+

R/utils.R

@@ -7,13 +7,14 @@
 #' 
 #' @return 
 #' A \code{data.frame} containing the following columns:
-#' \itemize{
-#'   \item{Dataset:} {the name of the function to load a dataset}
+#' \describe{
+#'   \item{Dataset}{The name of the function to load a dataset}
 #' }
 #' 
 #' @export
 #' 
 #' @importFrom utils read.csv
+#' @importFrom utils data
 #' 
 #' @examples
 #' availableDataSets()

inst/.gitignore

@@ -0,0 +1,12 @@
+.DS_Store
+inst/extras/
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
+inst/doc
+inst/scripts/*-original
+inst/scripts/microbiomeDataSets
+inst/scripts/*.gz
+*~
+